Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks

This paper presents the work carried out towards developing a diagnostic system for the identification of accident scenarios in 220 MWe Indian PHWRs. The objective of this study is to develop a methodology based on artificial neural networks (ANNs), which assists in identifying a transient quickly and suggests the operator to initiate the corrective actions during abnormal operations of the reactor. An operator support system, known as symptom-based diagnostic system (SBDS), has been developed using ANN that diagnoses the transients based on reactor process parameters, and continuously displays the status of the reactor. As a pilot study, the large break loss of coolant accident (LOCA) with and without the emergency core cooling system (ECCS) in reactor headers has been considered. Several break scenarios of large break LOCA have been analyzed. The time-dependent transient data have been generated using the RELAP5 thermal hydraulic code assuming an equilibrium core, which conforms to a realistic estimation. The diagnostic results obtained from the ANN study are satisfactory. These results have been incorporated in the SBDS software for operator assistance. A few important outputs of the SBDS have been discussed in this paper.     

Symptom based diagnostic system for nuclear power plant operations using artificial neural networks

Nuclear power plant experiences a number of transients during its operations. These transients may be due to equipment failure, malfunctioning of process systems and unavailability of safety systems. In such a situation, the plant may result into an abnormal state which is undesired. In case of an undesired plant condition generally known as an initiating event (IE), the operator has to carry out diagnostic and corrective actions. The operator's response may be too late to mitigate or minimize the negative consequences in such scenarios. The objective of this work is to develop an operator support system based on artificial neural networks that will assist the operator to identify the IEs at the earliest stages of their developments. These abnormal plant conditions must be diagnosed and identified through the process instrument readings. A symptom based diagnostic system has been developed to investigate the IEs. The event identification is carried out by using resilient back propagation neural network algorithm. Whenever an event is detected, the system will display the necessary operator actions in addition to the type of IE. The system will also show the graphical trend of relevant parameters. The developed system is able to identify the eight IEs of Narora Atomic Power Station. This paper describes the features of the diagnostic system taking one of the IEs as a case study.     

Water distribution under trickle irrigation predicted using artificial neural networks

An artificial neural network (ANN) technology is presented as an alternative to physical-based modeling of subsurface water distribution from trickle emitters. Three options are explored to prepare input–output functional relations from a database created using a numerical model (HYDRUS-2D). From the database the feasibility and advantages of the three alternative options are evaluated: water-content at defined coordinates, moment analysis describing the shape of the plume, and coordinates of individual water-content contours. The best option is determined in a way by the application objectives, but results suggest that prediction using moment analyses is probably the most versatile and robust and gives an adequate picture of the subsurface distribution. Of the other two options, the direct determination of the individual water contours was subjectively judged to be more successful than predicting the water content at given coordinates, at least in terms of describing the subsurface distribution. The results can be used to estimate subsurface water distribution for essentially any soil properties, initial conditions or flow rates for trickle sources.     

Efficiency parameters estimation in gemstones cut design using artificial neural networks

This paper deals with the problem of estimating cut results for faceted gemstones. The proposed approach applies artificial neural networks for a faceted gemstones analysis tool that could be further developed for incorporation in a computer-aided-design (CAD) context. Basic concepts concerning gemstone processing are introduced and the design of computational tools using neural networks is discussed. The model presented proposes two criteria to assess the efficiency of lapidary designs for rock crystal quartz: brilliance and yield. Closing the article, 62 different lapidary models were used to train and test the neural network tool.     

Design of artificial neural networks using a genetic algorithm to predict collection efficiency in venturi scrubbers

In this study, a new approach for the auto-design of neural networks, based on a genetic algorithm (GA), has been used to predict collection efficiency in venturi scrubbers. The experimental input data, including particle diameter, throat gas velocity, liquid to gas flow rate ratio, throat hydraulic diameter, pressure drop across the venturi scrubber and collection efficiency as an output, have been used to create a GA-artificial neural network (ANN) model. The testing results from the model are in good agreement with the experimental data. Comparison of the results of the GA optimized ANN model with the results from the trial-and-error calibrated ANN model indicates that the GA-ANN model is more efficient. Finally, the effects of operating parameters such as liquid to gas flow rate ratio, throat gas velocity, and particle diameter on collection efficiency were determined.     

Prediction of FRP-confined compressive strength of concrete using artificial neural networks

Strengthening and retrofitting of concrete columns by wrapping and bonding FRP sheets has become an efficient technique in recent years. Considerable investigations have been carried out in the field of FRP-confined concrete and there are many proposed models that predict the compressive strength which are developed empirically by either doing regression analysis using existing test data or by a development based on the theory of plasticity. In the present study, a new approach is developed to obtain the FRP-confined compressive strength of concrete using a large number of experimental data by applying artificial neural networks. Having parameters used as input nodes in ANN modeling such as characteristics of concrete and FRP, the output node was FRP-confined compressive strength of concrete. The idealized neural network was employed to generate empirical charts and equations for use in design. The comparison of the new approach with existing empirical and experimental data shows good precision and accuracy of the developed ANN-based model in predicting the FRP-confined compressive strength of concrete.     

人工神经网络在材料科学中的应用与展望

人工神经网络因其具有较强的非线性问题处理能力且容错性强而在材料科学中得到广泛的应用.本文对其在材料设计、材料制备工艺优化、塑性加工、热处理等领域的应用进行了探讨,并对其发展前景进行了展望.     

Hybrid artificial neural network based on BP-PLSR and its application in development of soft sensors

A novel hybrid artificial neural network (HANN) integrating error back propagation algorithm (BP) with partial least square regression (PLSR) was proposed to overcome two main flaws of artificial neural network (ANN), i.e. tendency to overfitting and difficulty to determine the optimal number of the hidden nodes. Firstly, single-hidden-layer network consisting of an input layer, a single hidden layer and an output layer is selected by HANN. The number of the hidden-layer neurons is determined according to the number of the modeling samples and the number of the neural network parameters. Secondly, BP is employed to train ANN, and then the hidden layer is applied to carry out the nonlinear transformation for independent variables. Thirdly, the inverse function of the output-layer node activation function is applied to calculate the expectation of the output-layer node input, and PLSR is employed to identify PLS components from the nonlinear transformed variables, remove the correlation among the nonlinear transformed variables and obtain the optimal relationship model of the nonlinear transformed variables with the expectation of the output-layer node input. Thus, the HANN model is developed. Further, HANN was employed to develop naphtha dry point soft sensor and the most important intermediate product concentration (i.e. 4-carboxybenzaldehyde concentration) soft sensor in p-xylene (PX) oxidation reaction due to the fact that there exist many factors having nonlinear effect on them and significant correlation among their factors. The results of two HANN applications show that HANN overcomes overfitting and has the robust character. And, the predicted squared relative errors of two optimal HANN models are all lower than those of two optimal ANN models and the mean predicted squared relative errors of HANN are lower than those of ANN in two applications.     

Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks

Understanding the circumstances under which drivers and passengers are more likely to be killed or more severely injured in an automobile accident can help improve the overall driving safety situation. Factors that affect the risk of increased injury of occupants in the event of an automotive accident include demographic or behavioral characteristics of the person, environmental factors and roadway conditions at the time of the accident occurrence, technical characteristics of the vehicle itself, among others. This study uses a series of artificial neural networks to model the potentially non-linear relationships between the injury severity levels and crash-related factors. It then conducts sensitivity analysis on the trained neural network models to identify the prioritized importance of crash-related factors as they apply to different injury severity levels. In the process, the problem of five-class prediction is decomposed into a set of binary prediction models (using a nationally representative sample of 30358 police-recorded crash reports) in order to obtain the granularity of information needed to identify the "true" cause and effect relationships between the crash-related factors and different levels of injury severity. The results, mostly validated by the findings of previous studies, provide insight into the changing importance of crash factors with the changing injury severity levels.     

Hierarchical supply chain planning using artificial neural networks to anticipate base-level outcomes

Most Advanced Planning Systems decompose the task of production planning according to the planning horizon in two levels, mid-term and short-term planning. The mid-term planning level sets the targets for the short-term level. In response, the short-term planning level gives feedback to the mid-term level. Moreover, due to detailed knowledge, the short-term planning level should provide relevant input to the mid-term planning run. To compute accurate targets for the short-term planning level the mid-term planning should anticipate its major behaviour. In this article we present an artificial neural network based anticipation of a short-term planning level for a single-stage, multi-product flow line production environment.     

基于模糊关联与BP网络的客户感性知识挖掘

通过建立Web问卷调查系统获取用户对产品造型特征的感性反映信息,并对用户感性评价信息予以模糊表征,进行多维模糊关联法则挖掘,进而产生客户感性信息与产品造型特征关联规则高频项目集。利用BP神经网络的学习能力对不同时段关联规则进行训练、预测和整合,从而实现客户感性知识挖掘,为产品设计辅助与企划决策支持提供新思路。     

Effective parameters modeling in compression of an austenitic stainless steel using artificial neural network

In this study, the prediction of flow stress in 304 stainless steel using artificial neural networks (ANN) has been investigated. Experimental data earlier deduced—by [S. Venugopal et al., Optimization of cold and warm workability in 304 stainless steel using instability maps, Metall. Trans. A 27A (1996) 126–199]—were collected to obtain training and test data. Temperature, strain-rate and strain were used as input layer, while the output was flow stress. The back propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. The results of this investigation shows that the R² values for the test and training data set are about 0.9791 and 0.9871, respectively, and the smallest mean absolute error is 14.235. With these results, we believe that the ANN can be used for prediction of flow stress as an accurate method in 304 stainless steel.     

Crushing behavior of laterally compressed composite elliptical tubes: Experiments and predictions using artificial neural networks

Composite materials have been increasingly used in the automobile industry for weight saving and part integration purposes. In this regard, composite elliptical tubes have been effectively employed as energy absorber devices. This increases the need for accurate and simple prediction techniques to optimize these structures.

The present work deals with the implementation of artificial neural networks (ANN) technique in the prediction of the crushing behavior and energy absorption characteristics of laterally loaded glass fiber/epoxy composite elliptical tubes. Predicted results are compared with actual experimental data in terms of load carrying capacity and energy absorption capability showing good agreement. This shows that ANN techniques could effectively be used to predict the response of collapsible composite energy absorber devices subjected to different loading conditions. As is the case for experimental findings, the predictions obtained using ANN also show the significant effect of the ellipticity ratio on the crushing behavior of laterally loaded tubes.     

基于BP人工神经网络的混凝土强度预测

混凝土强度是结构设计中控制的主要指标,其数值决定于水灰比、胶凝材料用量、矿物掺量、外加剂用量等多种因素,常规计算混凝土强度的公式因个人理解的不同而各异,一种仿生模型—人工神经网络则能很好地解决这个难题,文中尝试用人工神经网络对不同混凝土强度进行预测,结果表明此模型的可靠度很高,可以用以优化混凝土的试配,节约大量的时间、人力、物力和财力.     

Reliability analysis of laminated composite structures using finite elements and neural networks

Saving of computer processing time on the reliability analysis of laminated composite structures using artificial neural networks is the main objective of this work. This subject is particularly important when the reliability index is a constraint in the optimization of structural performance, because the task of looking for an optimum structural design demands also a very high processing time. Reliability methods, such as Standard Monte Carlo (SMC), Monte Carlo with Importance Sampling (MC–IS), First Order Reliability Method (FORM) and FORM with Multiple Check Points (FORM–MCPs) are used to compare the solution and the processing time when the Finite Element Method (FEM) is employed and when the finite element analysis (FEA) is substituted by trained artificial neural networks (ANNs). Two ANN are used here: the Multilayer Perceptron Network (MPN) and the Radial Basis Network (RBN). Several examples are presented, including a shell with geometrically non-linear behavior, which shows the advantages using this methodology.     

基于神经网络的长距离油气管道安全预警系统

提出了一种基于分布式光纤传感和人工神经网络判别的长距离输油管道安全预警系统.该系统利用光纤传感器收集管道周围土壤的振动信号,通过神经网络判断是否存在针对管道的破坏性行为和判别破坏性行为的类别,实现对油气管道的长距离安全预警.系统在预处理阶段对信号大幅度降维,降低数据处理的时间复杂度,以满足实时性的要求.在识别阶段则采用人工神经网络模型,包括反向传播(BP)网络和支持向量机(SVM).试验结果表明,这两种神经网络模型对打夯、镐刨、电钻三类破坏行为的识别率分别达到96.5和97.1%,均优于以往文献中的报道.     

Using ensemble and metaheuristics learning principles with artificial neural networks to improve due date prediction performance

One of the common and important problems in production scheduling is to quote an attractive but attainable due date for an arriving customer order. Among a wide variety of prediction methods proposed to improve due date quotation (DDQ) accuracy, artificial neural networks (ANN) are considered the most effective because of their flexible non-linear and interaction effects modelling capability. In spite of this growing use of ANNs in a DDQ context, ANNs have several intrinsic shortcomings such as instability, bias and variance problems that undermine their accuracy. In this paper, we develop an enhanced ANN-based DDQ model using machine learning, evolutionary and metaheuristics learning concepts. Computational experiments suggest that the proposed model outperforms the conventional ANN-based DDQ method under different shop environments and different training data sizes.     

An artificial neural network approach to multiphase continua constitutive modeling

Constitutive equations describe intrinsic relationships among sets of material system parameters. This study utilizes artificial neural networks in place of a traditional micromechanical approach to calculate the global (macroscopic) elastic properties of composite materials given the local (microscopic) properties and local geometry. This approach is shown to be more computationally efficient than conventional numerical micromechanical approaches. An eight sub-celled representative volume element is used for the local geometry. Multi target artificial neural networks (MTANNs) and single target artificial neural networks are studied for applicability in predicting the global properties. The best performing MTANN achieves a precision of 9%. The single target artificial neural networks (STANNs) perform best and predicts the global properties within a target error of 5.3%. The computation time is 1.8 s for all six STANNs to predict six global properties for 19,683 different microstructures.     

Predictive modelling of turning operations using response surface methodology,artificial neural networks and support vector regression

This paper focuses on developing empirical models for predicting surface roughness, tool wear and power required in turning operations. These response parameters are mainly dependent upon cutting velocity, feed and cutting time. Three competing data mining techniques, response surface methodology (RSM), artificial neural networks (ANN) and support vector regression (SVR), are applied in developing the empirical models. The data of 27 experiments have been used to generate, compare and evaluate the proposed models of tool wear, power required and surface roughness for the selected tool/material combination. Testing results demonstrate that the models developed in this research are suitable for predicting the response parameters with a satisfactory goodness of fit. It has been found that ANN and SVR models are much better than regression and RSM models for predicting the three response parameters. Finally, some future research directions are outlined.