共查询到20条相似文献，搜索用时 15 毫秒
1.
《International Journal of Mineral Processing》2005,75(1-2):53-68
The flow behavior in hydrocyclone is quite complex. This complexity of flow processes has led designers to rely on empirical equations for predicting the equipment performance. The publications on empirical models of the hydrocyclone far out-number few fluid-flow-modeling attempts. Empirical models correlate a classification parameter, such as the cut-size, with device dimensions and slurry properties. However, these can only be used within the extremes of the experimental data from which the model parameters were determined. On the other hand, models based on Computational Fluid Dynamics (CFD) techniques have proven to be useful in simulating fluid flow in hydrocyclones, and in predicting the separation efficiency of solid particles in the separator for a wide range of operating and design conditions. The shape and size of a hydrocyclone separator has a direct influence on the internal flow structure of the continuous phase and, thereby, the separation of the particulate phase. Hydrocylcones usually have a single inlet that distributes the feed stream near the end wall between the vortex finder and the sidewall. Effect of spigot diameter, i.e., 10 and 20 mm and inlet water velocities (5.91–12.35 m/s) on the water splits and particle classification in the hydrocyclone have been studied. The cut size of the hydrocyclone, operated at very low pulp density, has been predicted using discrete phase modeling technique. The studies revealed that with an increase in feed flow rate and decrease in spigot diameter the cyclone sharpness of separation improves. These predictions were found similar in line with the experimental observations. 相似文献
2.
敏感环境下基坑数值分析中土体本构模型的选择 总被引：15，自引：1，他引：14
数值分析已成为敏感环境下基坑工程分析的最重要手段,其关键是选择合适的土体本构模型和计算参数。在分析了岩土数值分析中常用土体本构模型特点的基础上,通过算例较系统地对比了各类模型在基坑开挖数值分析中的适用性。敏感环境下的基坑工程需重点关注墙后土体的变形,从满足工程需要和方便实用的角度出发,建议采用能考虑黏土的塑性和应变硬化特征、能区分加荷和卸荷且刚度依赖于应力水平的硬化类弹塑性模型,如MCC模型和HS模型进行分析。具体工程实例的分析,表明了硬化类弹塑性模型在敏感环境下基坑开挖数值分析中的适用性。 相似文献
3.
Numerous attempts have been made to modify the generalized constitutive model and to introduce new constitutive models in the framework of generalized plasticity. The modified models can predict the behavior of sand fairly well, however, such models require many parameters and are difficult to calibrate. Moreover, it is highly desirable for a model to be able to reproduce soil behavior using a single set of parameters. In this paper, the constitutive model by Pastor and Zienkiewicz is further developed based on critical state and bounding surface models. The model is used to simulate the behavior of three types of sands under monotonic and cyclic loadings. 相似文献
4.
Groundwater inflow assessment is essential for the design of tunnel drainage systems, as well as for assessment of the environmental impact of the associated drainage. Analytical and empirical methods used in current engineering practice do not adequately account for the effect of the jointed-rock-mass anisotropy and heterogeneity. The impact of geo-structural anisotropy of fractured rocks on tunnel inflows is addressed and the limitations of analytical solutions assuming isotropic hydraulic conductivity are discussed. In particular, the study develops an empirical correction to the analytical formula frequently used to predict groundwater tunnel inflow. In order to obtain this, a discrete network flow modelling study was carried out. Numerical simulation results provided a dataset useful for the calibration of some empirical coefficient to correct the well-known Goodman’s equation. This correction accounts for geo-structural parameters of the rock masses such as joint orientation, aperture, spacing and persistence. The obtained empirical equation was then applied to a medium-depth open tunnel in Bergamo District, northern Italy. The results, compared with the monitoring data, showed that the traditional analytical equations give the highest overestimation where the hydraulic conductivity shows great anisotropy. On the other hand, the empirical relation allows a better estimation of the tunnel inflow. 相似文献
5.
Newmark's sliding-block model is widely used to estimate coseismic slope performance. Early efforts to develop simple regression models to estimate Newmark displacement were based on analysis of the small number of strong-motion records then available. The current availability of a much larger set of strong-motion records dictates that these regression equations be updated. Regression equations were generated using data derived from a collection of 2270 strong-motion records from 30 worldwide earthquakes. The regression equations predict Newmark displacement in terms of (1) critical acceleration ratio, (2) critical acceleration ratio and earthquake magnitude, (3) Arias intensity and critical acceleration, and (4) Arias intensity and critical acceleration ratio. These equations are well constrained and fit the data well (71% < R2 < 88%), but they have standard deviations of about 0.5 log units, such that the range defined by the mean ± one standard deviation spans about an order of magnitude. These regression models, therefore, are not recommended for use in site-specific design, but rather for regional-scale seismic landslide hazard mapping or for rapid preliminary screening of sites. 相似文献
6.
《International Journal of Mineral Processing》2005,77(2):80-94
Classification studies to recover liberated particles of lead and zinc minerals from a ground lead–zinc ore were carried out using a 100-mm water-injection cyclone. The effects of variables on the performance of water-injection cyclone are discussed. The results indicated that the injection water rate has a complex interaction with the other variables like spigot opening, feed inlet pressure, and vortex finder opening. A set of empirical equations for slurry throughput, corrected cyclone cut size, minimum distribution point, and reduced efficiency numbers was developed for predicting the performance of water-injection cyclone. Finally, the results obtained in water-injection cyclone are compared with the data obtained on a similar diameter (100 mm) hydrocyclone. 相似文献
7.
A. Tahershamsi M. R. Majdzade Tabatabai R. Shirkhani 《International Journal of Environmental Science and Technology》2012,9(2):333-342
Regime width of alluvial channels is a vital problem in river morphology and channel design. Many equations are available in the literature to predict regime width of alluvial rivers. In general, there are many approaches to estimate regime width; however, none of them is widely accepted at present. This is due to the fact that most hypotheses have many constrains which may lead to simplify governing conditions and also lack of knowledge of some physical processes associated with channel formation and maintenance. Intelligent models are a new approach to describe complex problems one of which is artificial neural networks. In this research, initially, gravel bed rivers database was used in bankfull discharge condition to train various dimensional and non-dimensional neural-network schemes with three and four variables as input data, respectively. Then, the same database was applied to fit regression analysis to estimate regime width; this led to drive dimensional and non-dimensional equations. Finally, dimensional and non-dimensional neural-network models and regression equations were compared together based on 50% error bands with other dataset. Results show that neural network can adequately estimate the regime width in gravel bed rivers and multilayer perceptron network with one hidden layer and eight hidden neurons based on dimensional data set was selected as optimum network to predict regime width. A sensitivity analysis also shows that bankfull discharge has a greater influence on regime width of gravel bed channels than the other independent parameters in dimensional scheme of neural network. 相似文献
8.
Prediction and controlling of flyrock in blasting operation using artificial neural network 总被引：4，自引：1，他引：3
M. Monjezi Amir Bahrami Ali Yazdian Varjani Ahmad Reza Sayadi 《Arabian Journal of Geosciences》2011,4(3-4):421-425
Flyrock is one of the most hazardous events in blasting operation of surface mines. There are several empirical methods to predict flyrock. Low performance of such models is due to complexity of flyrock analysis. Existence of various effective parameters and their unknown relationships are the main reasons for inaccuracy of the empirical models. Presently, application of new approaches such as artificial intelligence is highly recommended. In this paper, an attempt has been made to predict and control flyrock in blasting operation of Sangan iron mine, Iran incorporating rock properties and blast design parameters using artificial neural network (ANN) method. A three-layer feedforward back-propagation neural network having 13 hidden neurons with nine input parameters and one output parameter were trained using 192 experimental blast datasets. It was also observed that in ascending order, blastability index, charge per delay, hole diameter, stemming length, powder factor are the most effective parameters on the flyrock. Reducing charge per delay caused significant reduction in the flyrock from 165 to 25 m in the Sangan iron mine. 相似文献
9.
Sarat Kumar Das Pijush Samui Akshaya Kumar Sabat T. G. Sitharam 《Environmental Earth Sciences》2010,61(2):393-403
The swelling pressure of soil depends upon various soil parameters such as mineralogy, clay content, Atterberg’s limits, dry
density, moisture content, initial degree of saturation, etc. along with structural and environmental factors. It is very
difficult to model and analyze swelling pressure effectively taking all the above aspects into consideration. Various statistical/empirical
methods have been attempted to predict the swelling pressure based on index properties of soil. In this paper, the computational
intelligence techniques artificial neural network and support vector machine have been used to develop models based on the
set of available experimental results to predict swelling pressure from the inputs; natural moisture content, dry density,
liquid limit, plasticity index, and clay fraction. The generalization of the model to new set of data other than the training
set of data is discussed which is required for successful application of a model. A detailed study of the relative performance
of the computational intelligence techniques has been carried out based on different statistical performance criteria. 相似文献
10.
Utilizing computational neural networks for evaluating the permeability of compacted clay liners 总被引：1，自引：0，他引：1
The design of lined waste-storage facilities is significantly influenced by the permeability of the liner. The permeability of compacted clay liners, in turn, is influenced by factors such as clay type and composition, compaction type and effort, and operating conditions. The complexity of the permeation process makes it difficult to predict analytically the permeability from these factors. As a result, empirical regression models are frequently used to predict permeability. In this paper, permeability prediction models are developed using computational neural networks (CNNS). The developed CNN models are used to predict the permeability of compacted clay for a known set of soil properties and field and laboratory conditions. Moreover, the models are used to determine the relative importance of the various input parameters to the model output. Also, a comparison between regression models and neural networks for predicting permeability is presented and the advantages of utilizing CNN methodology over regression techniques in model development are highlighted. 相似文献
11.
In the field of constitutive modelling of soil behaviour, optimisation techniques have been mostly employed as a calibration tool, particularly when several model parameters lack clear physical meaning. In this paper, however, a procedure based on a Hill-Climbing optimisation algorithm is presented as a form of improving the performance of constitutive models. Specifically, a simple cyclic nonlinear elastic model, which is shown to be unable to simulate adequately the damping ratio measured under small and large strain amplitudes, is modified by applying the Hill-Climbing technique to the determination of a new relationship describing the unloading/reloading behaviour of soil under cyclic loading. The performance of the proposed model is assessed by evaluating its parameters based on three distinct sets of empirical damping ratio curves and computing the corresponding error in their simulation. It is shown that the introduction of the new unloading/reloading expression formulated based on the outcome of the optimisation procedure increases substantially the precision of the constitutive model. 相似文献
12.
Prediction of water quality from simple field parameters 总被引：2，自引：0，他引：2
Water quality parameters like temperature, pH, total dissolved solids (TDS), total suspended solids (TSS), dissolved oxygen (DO), oil and grease, etc., are calculated from the field while parameters like biological oxygen demand (BOD) and chemical oxygen demand (COD) are interpreted through the laboratory tests. On one hand parameters like temperature, pH, DO, etc., can be accurately measured with the exceeding simplicity, whereas on the other hand calculation of BOD and COD is not only cumbersome but also inaccurate many times. A number of previous researchers have tried to use different empirical methods to predict BOD and COD but these empirical methods have their limitations due to their less versatile application. In this paper, an attempt has been made to calculate BOD and COD from simple field parameters like temperature, pH, DO, TSS, etc., using Artificial Neural Network (ANN) method. Datasets have been obtained from analysis of mine water discharge of one of the mines in Jharia coalfield, Jharkhand, India. 73 data sets were used to establish ANN architecture out of which 58 datasets were used to train the network while 15 datasets for testing the network. The results show encouraging similarity between experimental and predicted values. The RMSE values obtained for the BOD and COD are 0.114 and 0.983 %, respectively. 相似文献
13.
EPS颗粒轻质混合土的蠕变模型研究 总被引：1，自引：0，他引：1
EPS颗粒轻质混合土是一种新型轻质填土材料,相比EPS泡沫块体,具有更广泛的应用前景。EPS颗粒轻质混合土的基本物理及力学性质已得到了较多的研究,但其蠕变特性研究较少。通过不同围压下EPS颗粒轻质混合土的三轴不排水蠕变试验,采用三种常用的经验模型：Singh & Mitchell模型、Mesri模型和Findley模型对EPS颗粒轻质混合土的蠕变变形进行模拟,结果表明：Singh & Mithcell模型和Mesri模型能反映蠕变趋势,但是不能拟合应变初始快速上升段,用来描述EPS泡沫块体的Findley模型在应力水平较高时,拟合效果最好,某种程度上说,EPS颗粒轻质混合土的蠕变性与EPS泡沫块体相似。推导出了一种新的半经验半理论蠕变模型,用元件模型描述弹性变形部分,用经验模型描述塑性变形部分,拟合结果表明,不管应力水平的高低,新推蠕变模型都不仅能够很好地反映EPS颗粒轻质混合土的初始快速上升段变形,也能够比较准确地拟合长期变形,即蠕变性。 相似文献
14.
Exurbia from the bottom-up: Confronting empirical challenges to characterizing a complex system 总被引：2，自引：0，他引：2
Daniel G. Brown Derek T. Robinson Li An Moira Zellner William Rand Rick Riolo Bobbi Low 《Geoforum》2008,39(2):805-818
We describe empirical results from a multi-disciplinary project that support modeling complex processes of land-use and land-cover change in exurban parts of Southeastern Michigan. Based on two different conceptual models, one describing the evolution of urban form as a consequence of residential preferences and the other describing land-cover changes in an exurban township as a consequence of residential preferences, local policies, and a diversity of development types, we describe a variety of empirical data collected to support the mechanisms that we encoded in computational agent-based models. We used multiple methods, including social surveys, remote sensing, and statistical analysis of spatial data, to collect data that could be used to validate the structure of our models, calibrate their specific parameters, and evaluate their output. The data were used to investigate this system in the context of several themes from complexity science, including have (a) macro-level patterns; (b) autonomous decision making entities (i.e., agents); (c) heterogeneity among those entities; (d) social and spatial interactions that operate across multiple scales and (e) nonlinear feedback mechanisms. The results point to the importance of collecting data on agents and their interactions when producing agent-based models, the general validity of our conceptual models, and some changes that we needed to make to these models following data analysis. The calibrated models have been and are being used to evaluate landscape dynamics and the effects of various policy interventions on urban land-cover patterns. 相似文献
15.
基于Monte Carlo-BP神经网络TBM掘进速度预测 总被引：1，自引：0，他引：1
预测隧道工程中TBM掘进速度,主要有完全经验的、半理论半经验的模型和人工智能等方法,所用参数均为确定性的,未考虑参数存在的随机性,故导致预测结果的不准确性。基于此,提出了Monte Carlo-BP神经网络TBM掘进速度预测模型,着重考虑了一些重要输入参数的随机性, 其中输入参数重要性的大小通过粗糙集进行计算排序。采用Monte Carlo产生随机数时,由于参量的样本数据的有限,分布函数均采用阶梯形经验分布函数。如果采用的数据是来自不同类型的 TBM,则应当考虑机器性能参数,并重新对参数重要性进行排序。实例计算表明,Monte Carlo-BP神经网络模型预测结果和实测值总体趋势和均值比较一致。 相似文献
16.
Celestina Adu-Wusu Ernest K. Yanful Lisa Lanteigne Mike O’Kane 《Geotechnical and Geological Engineering》2007,25(2):215-237
Soil cover systems are widely used for containment of municipal solid waste, hazardous and mine waste, with the objective
of limiting the ingress of precipitation and oxygen. The ability to predict their long-term performance is crucial, as their
failure would result in the release of contaminants to the environment. However, monitoring covers over the long term to derive
the information needed to aid in design is impractical and there are no large-scale covers that have been in use for a long
enough period to generate the data needed. Numerical models have been particularly useful as design tools. To improve their
reliability these models may be calibrated to field data and then used to make long-term predictions of cover performance.
The field performance of two resistive test soil covers on a 20% sloping waste rock platform is predicted using the two-dimensional
soil–atmosphere model Vadose/W. Input data for the model included soil, climate and vegetation data obtained either in the
field or laboratory. Model results were compared to field data to assess the validity of the program. The model reasonably
simulated field response patterns for soil water storage and suction. Divergence between field performance data and model
predictions were significantly influenced by snowmelt, interflow and flow through preferential pathways. 相似文献
17.
《Geomechanics and Geoengineering》2013,8(1):53-61
In many rock engineering applications such as foundations, slopes and tunnels, the intact rock properties are not actually determined by laboratory tests, due to the requirements of high quality core samples and sophisticated test equipments. Thus, predicting the rock properties by using empirical equations has been an attractive research topic relating to rock engineering practice for many years. Soft computing techniques are now being used as alternative statistical tools. In this study, artificial neural network models were developed to predict the rock properties of the intact rock, by using sound level produced during rock drilling. A database of 832 datasets, including drill bit diameter, drill bit speed, penetration rate of the drill bit and equivalent sound level (Leq) produced during drilling for input parameters, and uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (ρ), P-wave velocity (Vp), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n) of intact rock for output, was established. The constructed models were checked using various prediction performance indices. Goodness of the fit measures revealed that recommended ANN model fitted the data as accurately as experimental results, indicating the usefulness of artificial neural networks in predicting rock properties. 相似文献
18.
Soil–water characteristic curve (SWCC) contains the fundamental information needed for describing the mechanical behavior of unsaturated soil. Some parameters such as air-entry value, slope at the inflection point, residual water content and residual suction are commonly used to describe the SWCC and other associated properties such as shear strength and permeability. Currently these parameters are determined using the graphical method which can be subjective and time consuming. Equations for determining these parameters are proposed and the relationships between SWCC parameters and fitting parameters are discussed in this paper. These equations can be used for computational analyses to replace the conventional graphical method in providing consistent results. 相似文献
19.
Vivek K. Himanshu M. P. Roy A. K. Mishra Ranjit Kumar Paswan Deepak Panda P. K. Singh 《Arabian Journal of Geosciences》2018,11(16):460
Excavation of coal, overburden, and mineral deposits by blasting is dominant over the globe to date, although there are certain undesirable effects of blasting which need to be controlled. Blast-induced vibration is one of the major concerns for blast designers as it may lead to structural damage. The empirical method for prediction of blast-induced vibration has been adopted by many researchers in the form of predictor equations. Predictor equations are site specific and indirectly related to physicomechanical and geological properties of rock mass as blast-induced ground vibration is a function of various controllable and uncontrollable parameters. Rock parameters for blasting face and propagation media for blast vibration waves are uncontrollable parameters, whereas blast design parameters like hole diameter, hole depth, column length of explosive charge, total number of blast holes, burden, spacing, explosive charge per delay, total explosive charge in a blasting round, and initiation system are controllable parameters. Optimization of blast design parameters is based on site condition and availability of equipment. Most of the smaller mines have predesigned blasting parameters except explosive charge per delay, total explosive charge, and distance of blast face from surface structures. However, larger opencast mines have variations in blast design parameters for different benches based on strata condition: Multivariate predictor equation is necessary in such case. This paper deals with a case study to establish multivariate predictor equation for Moher and Moher Amlohri Extension opencast mine of India. The multivariate statistical regression approach to establish linear and logarithmic scale relation between variables to predict peak particle velocity (PPV) has been used for this purpose. Blast design has been proposed based on established multivariate regression equation to optimize blast design parameters keeping PPV within legislative limits. 相似文献
20.
土-水特征曲线可以预测非饱和土的各种性质（如：非饱和渗透系数、剪应力和热学性能等）。但测量土-水特征曲线耗时久且花费昂贵。为了解决这一问题,目前,很多研究都致力于从基本的岩土工程性质预测土-水特征曲线。基于此,以MK（Modified Kovács）模型的2种形式（拟合方程和预测方程）为土-水特征曲线模型,以Matlab编程语言中的cftool为拟合工具,以西宁黄土、粉砂土、红黏土和冰碛土4种细粒土为研究对象,对比拟合方程和预测方程描述细粒土土-水特征曲线的效果和差异,分析MK模型中黏附饱和度 1解 的变化规律,提出了基于MK模型的饱和度进行参数敏感性分析的计算公式。结果表明：拟合曲线和预测曲线在描述4类典型细粒土土-水特征曲线时均具有较好的效果,但拟合曲线整体上优于预测曲线;土壤质地和黏粒含量影响 值;饱和度对拟合参数 的敏感性较大,对拟合参数 的敏感性较小。 相似文献