基于不同状态方程预测气体水合物相平衡条件

气相逸度的计算结果会直接影响气体水合物相平衡条件的预测精度。基于Chen-Guo模型,选取RK、SRK、PR以及PT四种状态方程计算逸度,分别对甲烷、乙烷以及二氧化碳三种不同气体水合物在不同温度范围内的相平衡条件进行计算。结果表明：纯水条件下,RK方程最适合预测甲烷水合物相平衡条件,而PR方程更适合预测乙烷及二氧化碳水合物相平衡条件;对于冰中,SRK方程适合预测甲烷水合物的相平衡条件,PR方程适合预测乙烷水合物的,而RK方程更适合二氧化碳水合物的;对于甲烷水合物,低于218.2 K的预测是导致模型预测精度偏低的原因;对于乙烷水合物,需要提高低于230.2 K的预测精度;对二氧化碳水合物而言,提高对低于270.7 K的预测可以进一步提高模型预测精度。     

水驱开发油田高含水期综合含水率灰色理论预测

水驱开发油田进入高含水生产阶段,综合含水率的预测对于油田开发动态分析以及注水开发方案调整就显得尤为重要。灰色预测理论实质上是一种动态趋势预测的定量化,用于油田综合含水率变化的宏观预测所需的样本（数据、项目）数较少而可靠性较高。本文从灰色理论的基本原理入手,阐述了综合含水率变化的灰色预测模型的建立过程,并以尕斯库勒油田深层油藏为例建立了灰色预测模型。     

Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks

Time-series prediction is one of themajor methodologies used for fault prediction. Themethods based on recurrent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping ability. As a new recurrent neural network, reservoir neural network can effectively process the time-series prediction. However, the ill-posedness problemof reservoir neural networks has seriously restricted the generalization performance. In this paper, a fault prediction algorithm based on time-series is proposed using improved reservoir neural networks. The basic idea is taking structure risk into consideration, that is, the cost function involves not only the experience risk factor but also the structure risk factor. Thus a regulation coefficient is introduced to calculate the outputweight of the reservoir neural network. As a result, the amplitude of outputweight is effectively controlled and the ill-posedness problemis solved. Because the training speed of ordinary reservoir networks is naturally fast, the improved reservoir networks for time-series prediction are good in speed and generalization ability. Experiments on Mackey-Glass and sunspot time series prediction prove the effectiveness of the algorithm. The proposed algorithm is applied to TE process fault prediction. We first forecast some timeseries obtained from TE and then predict the fault type adopting the static reservoirs with the predicted data. The final prediction correct rate reaches 81%.     

基于非等间距GM_GRNN组合模型的转炉应用

建立以终点碳和温度为间距的氧气用量非等间距灰色数列预测模型,并利用广义回归神经网络对灰模预测结果进行非线性组合优化,得到氧气质量的综合预测值.通过采集到的某钢厂实际生产数据,建立氧气质量的组合预测模型,得到平均相对精度达到97.39％的一步预测值.验证结果表明该组合模型是准确而有效的.     

焦炭预测系统在邯钢焦化厂的应用

通过小焦炉和铁箱试验对大焦炉质量进行预测,建立了一套比较成熟的焦炭预测模型。该模型对预测大焦炉焦炭质量具有很大的帮助。     

预测控制工程应用中预测时域的选取分析

对现有的预测时域选取方法进行了总结,并且结合工程实际和理论分析,给出了预测控制算法中预测时域可取的范围。在这一范围内,可以快速简捷地选取所有可取的预测时域。最后,通过两个仿真实例分析了在预测时域可取范围内,不同条件下选取的预测时域对于控制性能的影响。     

采用KPCA特征提取的近红外煤炭发热量预测模型

近红外光谱分析技术通过搭建基于GA-BP神经网络方法的定量分析模型,实现煤炭发热量的快速评估。为了提高模型的学习速度和精度,必须对光谱信息进行数据处理。该过程属于复杂的非线性问题,经典的线性主成分分析方法具有一定的局限性,因此采用了一种基于多项式核主成分分析特征提取方法。通过分析主成分的特征值筛选异常样本。实验结果表明,该方法提取的特征信息主成分集中度高、降维效果明显、与输出变量间的相关性好,且能够准确判断出异常样本,大幅度提高了模型的准确性,为近红外煤质分析模型提供了一种分析速度快、准确率高的有效数据处理方法。     

环境实验箱法及双指数模型法测定香水中TVOC

文章叙述环境试验箱法测定香水挥发的TVOC,引入双指数模型预测其挥发浓度、挥发效率,并建立评价体系,评价模型的预测准确度。研究结果显示模型预测的数据与实测数据相符,模型的预测准确度较高。因此,双指数模型适用于香水中TVOC释放量的预测。     

Prediction of cement strength using soft computing techniques

In this paper, it is aimed to propose prediction approaches for the 28-day compressive strength of Portland composite cement (PCC) by using soft computing techniques. Gene expression programming (GEP) and neural networks (NNs) are the soft computing techniques that are used for the prediction of compressive cement strength (CCS). In addition to these methods, stepwise regression analysis is also used to have an idea about the predictive power of the soft computing techniques in comparison to classical statistical approach. The application of the genetic programming (GP) technique GEP to the cement strength prediction is shown for the first time in this paper. The results obtained from the computational tests have shown that GEP is a promising technique for the prediction of cement strength.     

Analysis and prediction of indoor air pollutants in a subway station using a new key variable selection method

A new key variable selection and prediction model of IAQ that can select key variables governing indoor air quality (IAQ), such as PM₁₀, CO₂, CO, VOCs and formaldehyde, are suggested in this paper. The essential problem of the prediction model is the question of which of the original variables are the most important for predicting IAQ. The next issue is determining the number of key variables that should be ranked. A new index of discriminant importance in the projection (DIP) of Fisher??s linear discriminant (FLD) is suggested for selecting key variables of the prediction models with multiple linear regression (MLR) and partial least squares (PLS), as well as for ranking the importance of input measurement variables on IAQ prediction. The prediction models were applied to a real IAQ dataset from telemonitoring data (TMS) in a metro system. The prediction results of the model using all variables were compared with the results of the model using only key variables of DIP. It shows that the use of our new variable selection method cannot only reduce computational effort, but will also enhance the prediction performances of the models.     

Prediction Interval for Autoregressive Time Series via Oracally Efficient Estimation of Multi‐Step‐Ahead Innovation Distribution Function

A kernel distribution estimator (KDE) is proposed for multi‐step‐ahead prediction error distribution of autoregressive time series, based on prediction residuals. Under general assumptions, the KDE is proved to be oracally efficient as the infeasible KDE and the empirical cumulative distribution function (cdf) based on unobserved prediction errors. Quantile estimator is obtained from the oracally efficient KDE, and prediction interval for multi‐step‐ahead future observation is constructed using the estimated quantiles and shown to achieve asymptotically the nominal confidence levels. Simulation examples corroborate the asymptotic theory.     

采用神经网络和主成分分析方法建立催化重整装置收率预测模型

催化重整装置收率预测的准确性对生产计划的制定具有重要意义. 采用神经网络和主成分分析方法,并运用改进的遗传算法进行网络训练,建立了催化重整装置收率预测模型. 将这一模型程序作为催化重整装置收率预测模块加入软件GIOPIMS(Graphic I/O Petro-chemical Industry Modeling System)中,得到更为精确的产品收率预测结果. 在实际生产中,将收率预测模块计算的催化重整装置各侧线的收率代入生产计划模型中,可为生产计划的制定提供依据.     

Chaotic forecasting of time series of heat-transfer coefficient for an evaporator with a two-phase flow

The time series of heat-transfer film coefficient for an evaporator with a vapor-liquid two-phase boiling flow were forecasted by the chaotic prediction or the local nonlinear short-term prediction. The forecasting method was based on the phase space reconstruction theory and a mathematic model in the form of datum driving had been developed to carry out the prediction. The signals of heat-transfer film coefficients predicted by this method were compared with those obtained from the experimental measurements. Different from the previous work, the comparisons in this work were done both from the point of view of the time trajectory and from the point of view of macroscopic or general characters. Besides the time-averaged statistics characteristics, such as the average value, average deviation and standard deviation, the power spectrum, phase plane map and chaotic invariants including the correlation dimension and Kolmogorov entropy had been calculated and been compared for the time series obtained both from the experimental measurement and from the forecast. The comparison for each parameter between the value calculated from the time series measured and that estimated from the time series forecasted by the chaotic forecast method shows satisfactory agreement. The limited length in time with an accuracy prediction indicates that the system is chaotic. The agreement of the comparison of the general parameter indicates that the chaotic prediction is effective for the estimations of the heat transfer characteristics of the two-phase flow boiling system and that the chaotic prediction method may be a potential tool for the effective thermo-fluid control for such evaporators.     

An Error Analysis of Failure Prediction Techniques Derived From Fracture Mechanics

Three principal methods of failure prediction for brittle materials are analyzed statistically. Each method depends on fracture mechanics for its predictive value; hence the variance of the failure time depends on the scatter in the fracture mechanics data and the scatter in the estimate of the initial size of the strength-limiting crack. The variance is used to calculate confidence limits for the prediction of failure for glass and SiC. Procedures for the collection and analysis of data are discussed and the implications of the analysis for lifetime prediction are evaluated.     

Application of MIA for a QSAR Study of Inhibitory Activity of DHP Derivatives and Design of New Compounds Using WT and GA for Pixel Processing

A quantitative structure-activity relationship (QSAR) modeling was carried out for the prediction of inhibitory activity of dihydropyridine (DHP) derivatives known as calcium channel blocker (CCB) drugs. Partial least squares (PLS) algorithm was used for prediction of inhibitory activity of calcium channel antagonists as a function of the bidimensional images. In the present study, it is investigated that the effect of pixel selection by application of genetic algorithms (GAs) for PLS model, because of the GAs is very useful in the variable selection in modeling. Pre-processing methods such as wavelet transform (WT) were also used to enhance the predictive power of multivariate calibration methods. The subset of pixels, which resulted in the low prediction error, was selected by GA. To evaluate the models applied in this study (PLS, GA-PLS and WT-GA-PLS), the inhibitory activities of several compounds, not included in the modeling procedure, were predicted. The results of models showed high prediction ability with root mean square error of prediction (RMSEP) of 0.51, 0.39 and 0.17 for PLS, GA-PLS and WT-GA-PLS, respectively. The WT-GA-PLS method was employed to predict the inhibitory activity of the new antagonists.     

Dry colour prediction of leather from its wet state

The present investigation describes the development of a regression model for dry colour prediction based on the colour of wet leather. Such a prediction will be useful in the colour matching of leather – the time delay in drying of wet leather and determination of the final colour can be avoided. The use of computer technology interfaced with a reflectance spectrophotometer provides a novel approach to the prediction of dry colour from wet colour. In this study, five different regression models, i.e. linear fit, log fit, quadratic fit, log quadratic fit and cubic fit, were developed, with the dry reflectance of the leather sample as a dependent variable and the wet reflectance of the leather as an independent variable. The relationship between moisture content and surface leather colour, a prerequisite for enabling prediction, was studied. The obtained log quadratic model can be used for accurate prediction of dry colour from wet colour.     

综合参数法预测产量

介绍预测新井稳产产量的方法,从而可以找出万吨以上的产区。该油田新井产量的综合参数预测方法具有所需样本少、预测精度高、计算速度快和操作方便等优点,特别适用于随机性强的新井生产系统有关指标的预测,它不仅对预测所需的数据多少不限,而且也无须剔除不可比因素,只要物性参数合理,测量误差不大,预测结果就可以信赖。     

Improved Prediction Limits For AR(p) and ARCH(p) Processes

Abstract. A new simulation‐based prediction limit that improves on any given estimative d‐step‐ahead prediction limit for a Markov process is described. This improved prediction limit can be found with almost no algebraic manipulations. Nonetheless, it has the same asymptotic coverage properties as the Barndorff‐Nielsen and Cox [Inference and Asymptotics (1994) Chapman and Hall, London] and Vidoni [Journal of Time Series Analysis Vol. 25, pp. 137–154.] (2004) improved prediction limits. The new simulation‐based prediction limit is ideally suited to those Markov process models for which the algebraic manipulations required for the latter improved prediction limits are very complicated. We illustrate the new method by applying it in the context of one‐step‐ahead prediction for a zero‐mean Gaussian AR(2) process and an ARCH(2) process.     

Prediction of protein-protein interaction sites using support vector machines

The identification of protein-protein interaction sites is essential for the mutant design and prediction of protein-protein networks. The interaction sites of residue units were predicted using support vector machines (SVM) and the profiles of sequentially/spatially neighboring residues, plus additional information. When only sequence information was used, prediction performance was highest using the feature vectors, sequentially neighboring profiles and predicted interaction site ratios, which were calculated by SVM regression using amino acid compositions. When structural information was also used, prediction performance was highest using the feature vectors, spatially neighboring residue profiles, accessible surface areas, and the with/without protein interaction sites ratios predicted by SVM regression and amino acid compositions. In the latter case, the precision at recall = 50% was 54-56% for a homo-hetero mixed test set and >20% higher than for random prediction. Approximately 30% of the residues wrongly predicted as interaction sites were the closest sequentially/spatially neighboring on the interaction site residues. The predicted residues covered 86-87% of the actual interfaces (96-97% of interfaces with over 20 residues). This prediction performance appeared to be slightly higher than a previously reported study. Comparing the prediction accuracy of each molecule, it seems to be easier to predict interaction sites for stable complexes.