基于BP神经网络的天然气水合物相平衡计算及预测

针对天然气水合物相平衡问题,文中提出用基于带动量因子的BP神经网络进行计算和预测。首先用遗传算法优化确定BP神经网络的结构和参数,得到最优化结构的神经网络;其次结合Levenberg-Marquart优化算法,建立天然气水合物相平衡计算及预测的神经网络模型;最后以实验测定的(CH4 CO2 H2S)三元酸性天然气水合物体系的平衡数据为训练和预测样本进行了计算。计算表明,预测结果与实验数据有良好的一致性,而且由于BP神经网络作为所谓的“纯粹”的算法不需要热力学模型,这对于相平衡计算是非常方便的,所以是研究天然气水合物相平衡计算及预测的一种新的有效方法。     

Predicting the capability-polar-plots for dynamic positioning systems for offshore platforms using artificial neural networks

As the capability of polar plots becomes better understood, improved dynamic positioning (DP) systems are possible as the control algorithms greatly depend on the accuracy of the aerodynamic and hydrodynamic models. The measurements and estimation of the environmental disturbances have an important role in the optimal design and selection of a DP system for offshore platforms. The main objective of this work is to present a new method of predicting the Capability-Polar-Plots for offshore platforms using the combination of the artificial neural networks (NNs) and the capability polar plots program (CPPP). The estimated results from a case study for a scientific drilling vessel are presented. A trained artificial NN is designed in this work and is able to predict the maximum wind speed at which the DP thrusters are able to maintain the offshore platform in a station-keeping mode in the field site. This prediction for the maximum wind speed will be a helpful tool for DP operators in managing station-keeping for offshore platforms in an emergency situation where the automation of the DP systems is disabled. It is obvious from the obtained results that the developed technique has potential for the estimation of the capability-polar-plots for offshore platforms. This tool would be suitable for DP operators to predict the maximum wind speed and direction in a very short period of time.     

南黄海和东海“人工水母”投放试验

1984—1986年,作者在南黄海和东海投放大量“人工水母”测量底层流。本文介绍了“人工水母”的投放、回收和漂流概况,根据这一实验结果,参考有关黄、东海海流研究的成果,绘出了调查试验海区的底层流模式。     

杜氏鰤人工苗养殖生物学特性及养殖技术研究

报道杜氏鰤(Seriola dumerili)人工苗养殖生物学特性及养殖技术研究结果。在海区水温6-30．5℃。海水盐度14．33，溶解氧6．5-7．3mg／L，pH7．5-8．1，水流为15-35cm／s的网箱中，杜氏鰤人工苗叉长40-50mm，体质量1．5-1．7g，经208d养殖，叉长达到330-388mm，体质量达到950-1400g，饲料系数为11，成活率为71．7％；养殖720d，体质量达4．5-6．5k，饲料系数为8；养殖l080d，体质量达7．5-11．5kg，饲料系数为7；养殖l440d，体质量达13．，15．5k异，饲料系数为6；养殖l800d，体质量达15．5-18．5kg，饲料系数为5。第4年性腺发育成熟，4-5月份，海上网箱人工催产，平均每尾雌鱼可产卵1．8kg左右。     

Artificial neural network to translate offshore satellite wave data to coastal locations

Owing to the spatial averaging involved in satellite sensing, use of observations so collected is often restricted to offshore regions. This paper discusses a technique to obtain significant wave heights at a specified coastal site from their values gathered by a satellite at deeper offshore locations. The technique is based on the approach of Artificial Neural Network (ANN) of Radial Basis Function (RBF) and Feed-forward Back-propagation (FFBP) type. The satellite-sensed data of significant wave height; average wave period and the wind speed were given as input to the network in order to obtain significant wave heights at a coastal site situated along the west coast of India. Qualitative as well as quantitative comparison of the network output with target observations showed usefulness of the selected networks in such an application vis-à-vis simpler techniques like statistical regression. The basic FFBP network predicted the higher waves more correctly although such a network was less attractive from the point of overall accuracy. Unlike satellite observations collection of buoy data is costly and hence, it is generally resorted to fewer locations and for a smaller period of time. As shown in this study the network can be trained with samples of buoy data and can be further used for routine wave forecasting at coastal locations based on more permanent flow of satellite observations.     

Prediction of uniaxial compressive strength of sandstones using petrography-based models

The uniaxial compressive strength of intact rock is the main parameter used in almost all engineering projects. The uniaxial compressive strength test requires high quality core samples of regular geometry. The standard cores cannot always be extracted from weak, highly fractured, thinly bedded, foliated and/or block-in-matrix rocks. For this reason, the simple prediction models become attractive for engineering geologists. Although, the sandstone is one of the most abundant rock type, a general prediction model for the uniaxial compressive strength of sandstones does not exist in the literature. The main purposes of the study are to investigate the relationships between strength and petrographical properties of sandstones, to construct a database as large as possible, to perform a logical parameter selection routine, to discuss the key petrographical parameters governing the uniaxial compressive strength of sandstones and to develop a general prediction model for the uniaxial compressive strength of sandstones. During the analyses, a total of 138 cases including uniaxial compressive strength and petrographic properties were employed. Independent variables for the multiple prediction model were selected as quartz content, packing density and concavo–convex type grain contact. Using these independent variables, two different prediction models such as multiple regression and ANN were developed. Also, a routine for the selection of the best prediction model was proposed in the study. The constructed models were checked by using various prediction performance indices. Consequently, it is possible to say that the constructed models can be used for practical purposes.     

Prediction of relative crest settlement of concrete-faced rockfill dams analyzed using an artificial neural network model

A neural network model has been developed for the prediction of relative crest settlement (RCS) of concrete-faced rockfill dams (CFRDs) using 30 databases of field data from seven countries (of which 21 were used for training and 9 for testing). The settlement values predicted using the optimum artificial neural network (ANN) model are in good agreement with these field data. A database prepared from reported crest settlement values of CFRDs after construction was used to train the ANN model to predict the RCS. It is demonstrated here that the model is capable of predicting accurately the relative crest settlement of CFRDs and is potentially applicable for general usage with knowledge of the three basic properties of a dam (void ratio, e; height, H; and vertical deformation modulus, E_V).

The performance of the new ANN model is compared with that of conventional methods based on the Clements theory and also with that of a proposed equation derived from the field data. The comparison indicates that the ANN model has strong potential and offers better performance than conventional methods when used as a quick interpolation and extrapolation tool. The conventional calculation model was proposed based on the fixed connection weights and bias factors of the optimum ANN structure. This method can support the dam engineer in predicting the relative crest settlement of a CFRD after impounding.     

Modeling and prediction of ventilation methane emissions of U.S. longwall mines using supervised artificial neural networks

Methane emissions from a longwall ventilation system are an important indicator of how much methane a particular mine is producing and how much air should be provided to keep the methane levels under statutory limits. Knowing the amount of ventilation methane emission is also important for environmental considerations and for identifying opportunities to capture and utilize the methane for energy production.Prediction of methane emissions before mining is difficult since it depends on a number of geological, geographical, and operational factors. This study proposes a principle component analysis (PCA) and artificial neural network (ANN)-based approach to predict the ventilation methane emission rates of U.S. longwall mines.Ventilation emission data obtained from 63 longwall mines in 10 states for the years between 1985 and 2005 were combined with corresponding coalbed properties, geographical information, and longwall operation parameters. The compiled database resulted in 17 parameters that potentially impacted emissions. PCA was used to determine those variables that most influenced ventilation emissions and were considered for further predictive modeling using ANN. Different combinations of variables in the data set and network structures were used for network training and testing to achieve minimum mean square errors and high correlations between measurements and predictions. The resultant ANN model using nine main input variables was superior to multilinear and second-order non-linear models for predicting the new data. The ANN model predicted methane emissions with high accuracy. It is concluded that the model can be used as a predictive tool since it includes those factors that influence longwall ventilation emission rates.     

Theoretical Concept to Understand Plan and Design Smooth Blasting Pattern

Considering different mechanical cutting tools for excavation of rock, drilling and blasting is said to be inexpensive and at the same time most acceptable and compatible to any geo-excavation condition. Depending upon strength properties of in-situ rock mass, characteristics of joint pattern and required quality of blasting, control blasting techniques viz., pre-split and smooth blasting are commonly implemented to achieve an undamaged periphery rock-wall. To minimize magnitude of damage or overbreak, the paper emphasized that in-situ stresses and re-distribution of stresses during the process of excavation should be considered prior to selection of explosive parameters and implementation of any suitable blast pattern. Rock structure being not massive in nature, the paper firstly explains the influence of discontinuities and design parameters on smooth-wall blasting. Considering the empirical equations for estimation of stress wave’s magnitude and its attenuation characteristics through transmitting medium, the paper has put forward a mathematical model for smooth blasting pattern. The model firstly illustrates that rock burden for each hole should be sub-divided into thin micro strips/slabs to understand the characteristics of wave transmission through the medium and lastly with the help of beam theory of structural dynamics have put forward a mathematical model to analyze and design an effective smooth blasting pattern to achieve an undamaged periphery rock-wall.     

定向爆破拆除框架厂房

结合开封市龙亭水泥厂旧厂房爆破拆除工程,阐述定向爆破方法拆除框架厂房的设计原理和施工要求。